Development of Machine Learning Algorithms for Identifying Patients With Limited Health Literacy

作者：Dylan Koole, Oscar Shen, Amanda Lans, Tom M. de Groot, Jorrit‐Jan Verlaan, Joseph H. Schwab · 发表于：Journal of Evaluation in Clinical Practice · 年份：2024 · DOI：10.1111/jep.14248 · 被引用次数：3 · 研究领域：Health Literacy and Information Accessibility、Machine Learning in Healthcare、Mental Health via Writing

RATIONALE: Limited health literacy (HL) leads to poor health outcomes, psychological stress, and misutilization of medical resources. Although interventions aimed at improving HL may be effective, identifying patients at risk of limited HL in the clinical workflow is challenging. With machine learning (ML) algorithms based on readily available data, healthcare professionals would be enabled to incorporate HL screening without the need for administering in-person HL screening tools. AIMS AND OBJECTIVES: Develop ML algorithms to identify patients at risk for limited HL in spine patients. METHODS: Between December 2021 and February 2023, consecutive English-speaking patients over the age of 18 and new to an urban academic outpatient spine clinic were approached for participation in a cross-sectional survey study. HL was assessed using the Newest Vital Sign and the scores were divided into limited (0-3) and adequate (4-6) HL. Additional patient characteristics were extracted through a sociodemographic survey and electronic health records. Subsequently, feature selection was performed by random forest algorithms with recursive feature selection and five ML models (stochastic gradient boosting, random forest, Bayes point machine, elastic-net penalized logistic regression, support vector machine) were developed to predict limited HL. RESULTS: Seven hundred and fifty-three patients were included for model development, of whom 259 (34.4%) had limited HL. Variables identified for predi...